Chemical space contains an immense number of possible molecules and materials, making exhaustive exploration infeasible. This framework combines topological data analysis with deep learning, using Mapper to reveal the organization of molecular representations learned by geometric deep learning models. It exposes clusters, linkages, and branching structures associated with molecular scaffolds, functional groups, chemical properties, and structural and functional evolution.